Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

EURASIP Journal on Image and Video Processing, Aug 2017

Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach.

A PDF file should load here. If you do not see its contents the file may be temporarily unavailable at the journal website or you do not have a PDF plug-in installed and enabled in your browser.

Alternatively, you can download the file locally and open with any standalone PDF reader:

https://link.springer.com/content/pdf/10.1186%2Fs13640-017-0203-4.pdf

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

Alkinani and El-Sakka EURASIP Journal on Image and Video Processing Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction Monagi H. Alkinani 1 Mahmoud R. El-Sakka 0 0 Department of Computer Science, Middlesex College, Western University , 1151 Richmond Street, N6A 5B7, London, Ontario , Canada 1 Department of Computer Science, University of Jeddah , Asfan Road, 285, Dhahban 23881, Jeddah , Saudi Arabia Background: Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. Methods: We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Results: Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Conclusion: Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach. Patch-based image denoising; Bilateral filter; Non-local means filtering; Probabilistic patch-based filtering; Dictionary learning filtering; K-SVD; Gaussian patch-PCA filtering; BM3D 1 Review 1.1 Introduction The noise level in digital images may vary from being almost imperceptible to being very noticeable. Image denoising techniques attempt to produce a new image that has less noise, i.e., closer to the original noise-free image. Image denoising techniques can be grouped into two main approaches: pixel-based image filtering and patch-based image filtering. A pixel-based image filtering scheme is mainly a proximity operation used for manipulating one pixel at a time (pixel-wise) based on its spatial neighboring pixels located within a kernel. On the other hand, in patch-based image filtering, the noisy image is divided into patches, or “blocks,” which are then manipulated separately in order to provide an estimate of the true pixel values (patch-wise) based on similar patches located within a search window. This approach utilizes the redundancy and the similarity among the various parts of the input image. Figure 1 shows the mechanism of the two approaches. It is now common in image denoising field to utilize patch-based models and algorithms instead of pixelbased approaches to produce most promising estimate of the noise-free images. However, there are both advantages and disadvantages in the use of patch-based models and algorithms. There are several advantages of patchbased approaches. Smoothing flat regions is the most important aspect. Redundancy between patches enable patch-based approaches to properly smooth flat reigns. A second advantage of using patch-based models and algorithms approaches is that it can preserve fine image details and sharp edges. However, there could be some disadvantages for patch-based models and algorithms. First, although similarity between patches assists in estimating flat regions, so is the averaging. It is, therefore, quite timeconsuming to group and compare similar patches. This might mean that each patch has multiple estimates and patches are overlapped. Secondly, while it may be that patterns and textures seem clear with less noise, patchbased models and algorithms usually exploit large number of parameters, which can be extremely difficult to adjust properly. We believe that the advantages of patch-based methods far outweigh their disadvantages, as modern computers are significantly fast, and have large memory spaces. In this work, the patch-based image denoising schemes are analyzed from two different aspects: (1) the performance of patch-based denoising techniques in terms of image denoising quality and (2) the performance of patchbased denoising techniques in terms of computational time, where various patch-based denoising techniques are addressed. A literature survey was condu (...truncated)


This is a preview of a remote PDF: https://link.springer.com/content/pdf/10.1186%2Fs13640-017-0203-4.pdf

Monagi H. Alkinani, Mahmoud R. El-Sakka. Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction, EURASIP Journal on Image and Video Processing, 2017, Volume 2017, Issue 1, DOI: 10.1186/s13640-017-0203-4